DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Factors

> ds <- data.frame(age=c(34, 56, 23, 72, 48), 
                   risk=c("high", "low", "high", "low", "high"))
> ds
  age risk
1  34 high
2  56  low
3  23 high
4  72  low
5  48 high
> levels(ds$risk)
[1] "high" "low"

By default levels within a factor are not ordered:

> ds$age[1] < ds$age[2]
[1] TRUE
> ds$risk[1] < ds$risk[2]
[1] NA
Warning message:
< not meaningful for factors in: Ops.factor(ds$risk[1], ds$risk[2])

We can order the levels using the ordered function:

> ds$risk <- ordered(ds$risk)
> levels(ds$risk)
[1] "high" "low" 
> ds$risk[1] < ds$risk[2]
[1] TRUE

Saying that high is less than low is probably not what we wanted. The ordering used is the same as what levels returns.

You can change the names of the levels by assigning to the levels call:

> levels(ds$risk) <- c("upper", "lower")
> ds
  age  risk
1  34 upper
2  56 lower
3  23 upper
4  72 lower
5  48 upper

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.